153 lines
5.0 KiB
Python
153 lines
5.0 KiB
Python
import base64
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import json
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from openai import AsyncOpenAI
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from app.ai.base import VisionProvider, TextProvider, VisionResult, DateResult, LeafletResult
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from app.config import settings
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class OpenAIVisionProvider(VisionProvider):
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def __init__(self):
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self.client = AsyncOpenAI(api_key=settings.OPENAI_API_KEY)
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self.model = settings.OPENAI_MODEL
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async def recognize_medicine(self, image_bytes: bytes) -> VisionResult:
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base64_image = base64.b64encode(image_bytes).decode('utf-8')
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response = await self.client.chat.completions.create(
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model=self.model,
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": """请识别这张药品包装图片中的信息，返回JSON格式：
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{
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"generic_name": "通用名称",
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"brand_name": "商品名称",
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"manufacturer": "生产厂家",
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"specification": "规格"
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}
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只提取图片中真实出现的内容，不要猜测。"""
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},
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{base64_image}"
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}
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}
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]
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}
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],
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response_format={"type": "json_object"}
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)
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result = json.loads(response.choices[0].message.content)
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return VisionResult(**result)
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async def recognize_dates(self, image_bytes: bytes) -> DateResult:
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base64_image = base64.b64encode(image_bytes).decode('utf-8')
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response = await self.client.chat.completions.create(
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model=self.model,
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": """请识别这张图片中的日期信息，返回JSON格式：
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{
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"production_date": "生产日期(YYYY-MM-DD格式，如果无法识别则为null)",
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"expiry_date": "有效期/过期日期(YYYY-MM-DD格式，如果无法识别则为null)"
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}
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只提取图片中真实出现的日期，不要猜测或推理。"""
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},
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{base64_image}"
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}
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}
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]
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}
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],
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response_format={"type": "json_object"}
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)
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result = json.loads(response.choices[0].message.content)
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return DateResult(**result)
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class OpenAITextProvider(TextProvider):
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def __init__(self):
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self.client = AsyncOpenAI(api_key=settings.OPENAI_API_KEY)
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self.model = "gpt-4"
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async def summarize_leaflet(self, text: str) -> LeafletResult:
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response = await self.client.chat.completions.create(
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model=self.model,
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messages=[
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{
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"role": "system",
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"content": "你是一个医疗信息提取助手。请从药品说明书中提取关键信息。"
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},
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{
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"role": "user",
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"content": f"""请从以下药品说明书中提取关键信息，返回JSON格式：
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{{
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"indications": "适应症",
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"adult_dose": "成人用法用量",
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"child_dose": "儿童用法用量(如果没有则为null)",
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"contraindications": "禁忌",
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"notes": "注意事项(如果有)"
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}}
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说明书内容：
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{text}"""
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}
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],
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response_format={"type": "json_object"}
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)
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result = json.loads(response.choices[0].message.content)
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return LeafletResult(**result)
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async def natural_language_search(self, query: str, medicines: list) -> list:
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medicines_text = "\n".join([
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f"- {m['name']}: {m.get('indications', '')}"
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for m in medicines
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])
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response = await self.client.chat.completions.create(
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model=self.model,
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messages=[
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{
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"role": "system",
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"content": "你是一个药品搜索助手。根据用户描述的症状，从药品列表中找出可能适用的药品。"
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},
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{
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"role": "user",
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"content": f"""用户描述：{query}
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可用药品列表：
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{medicines_text}
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请返回JSON格式的搜索结果：
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{{
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"results": [
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{{
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"medicine_id": 药品ID,
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"name": "药品名称",
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"reason": "匹配原因"
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}}
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]
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}}"""
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}
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],
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response_format={"type": "json_object"}
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)
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result = json.loads(response.choices[0].message.content)
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return result.get('results', []) |